## datatable function from DT package create an HTML widget display of the dataset
## install DT package if the package is not yet available in your R environment
readxl::read_excel("dataset/dataset-variable-description.xlsx") |>
DT::datatable()BCON147_MIDTERM_PROJECT_EXERCISE
BCon 147: special topics
1 Project overiew
In this project, we will explore employee attrition and performance using the HR Analytics Employee Attrition & Performance dataset. The primary goal is to develop insights into the factors that contribute to employee attrition. By analyzing a range of factors, including demographic data, job satisfaction, work-life balance, and job role, we aim to help businesses identify key areas where they can improve employee retention.
2 Scenario
Imagine you are working as a data analyst for a mid-sized company that is experiencing high employee turnover, especially among high-performing employees. The company has been facing increased costs related to hiring and training new employees, and management is concerned about the negative impact on productivity and morale. The human resources (HR) team has collected historical employee data and now looks to you for actionable insights. They want to understand why employees are leaving and how to retain talent effectively.
Your task is to analyze the dataset and provide insights that will help HR prioritize retention strategies. These strategies could include interventions like revising compensation policies, improving job satisfaction, or focusing on work-life balance initiatives. The success of your analysis could lead to significant cost savings for the company and an increase in employee engagement and performance.
3 Understanding data source
The dataset used for this project provides information about employee demographics, performance metrics, and various satisfaction ratings. The dataset is particularly useful for exploring how factors such as job satisfaction, work-life balance, and training opportunities influence employee performance and attrition.
This dataset is well-suited for conducting in-depth analysis of employee performance and retention, enabling us to build predictive models that identify the key drivers of employee attrition. Additionally, we can assess the impact of various organizational factors, such as training and work-life balance, on both performance and retention outcomes.
4 Data wrangling and management
Libraries
Before we start working on the dataset, we need to load the necessary libraries that will be used for data wrangling, analysis and visualization. Make sure to load the following libraries here. For packages to be installed, you can use the install.packages function. There are packages to be installed later on this project, so make sure to install them as needed and load them here.
# load all your libraries here
#install.packages("magrittr")
library(magrittr)
#install.packages("dplyr")
library(dplyr)
#install.packages("tidyverse")
library(tidyverse)
#install.packages("ggplot2")
library(ggplot2)
#install.packages("readr")
library(readr)
#install.packages("DT")
library(DT)
#install.packages("janitor")
library(janitor)
#install.packages("GGally")
library(GGally)
#install.packages("sjPlot")
library(sjPlot)
#install.packages("report")
library(report)
#install.packages("ggstatsplot")
library(ggstatsplot)4.1 Data importation
Import the two dataset
Employee.csvandPerformanceRating.csv. Save theEmployee.csvasemployee_dtaandPerformanceRating.csvasperf_rating_dta.Merge the two dataset using the
left_joinfunction fromdplyr. Use theEmployeeIDvariable as the varible to join by. You may read more information about theleft_joinfunction here.Save the merged dataset as
hr_perf_dtaand display the dataset using thedatatablefunction fromDTpackage.
## import the two data here
employee_dta <- read_csv("dataset/Employee.csv")
perf_rating_dta <- read_csv("dataset/PerformanceRating.csv")
## merge employee_dta and perf_rating_dta using left_join function.
## save the merged dataset as hr_perf_dta
hr_perf_dta <- left_join(employee_dta, perf_rating_dta, by = "EmployeeID")
## Use the datatable from DT package to display the merged dataset
datatable(hr_perf_dta)4.2 Data management
Using the
clean_namesfunction fromjanitorpackage, standardize the variable names by using the recommended naming of variables.Save the renamed variables as
hr_perf_dtato update the dataset.
## clean names using the janitor packages and save as hr_perf_dta
library(janitor)
hr_perf_dta <- hr_perf_dta %>% clean_names()
## display the renamed hr_perf_dta using datatable function
datatable(hr_perf_dta)Create a new variable
cat_educationwhereineducationis1=No formal education;2=High school;3=Bachelor;4=Masters;5=Doctorate. Use thecase_whenfunction to accomplish this task.Similarly, create new variables
cat_envi_sat,cat_job_sat, andcat_relation_satforenvironment_satisfaction,job_satisfaction, andrelationship_satisfaction, respectively. Re-code the values accordingly as1=Very dissatisfied;2=Dissatisfied;3=Neutral;4=Satisfied; and5=Very satisfied.Create new variables
cat_work_life_balance,cat_self_rating,cat_manager_ratingforwork_life_balance,self_rating, andmanager_rating, respectively. Re-code accordingly as1=Unacceptable;2=Needs improvement;3=Meets expectation;4=Exceeds expectation; and5=Above and beyond.Create a new variable
bi_attritionby transformingattritionvariable as a numeric variabe. Re-code accordingly asNo=0, andYes=1.Save all the changes in the
hr_perf_dta. Note that saving the changes with the same name will update the dataset with the new variables created.
## create cat_education
str(hr_perf_dta)spc_tbl_ [6,899 x 33] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ employee_id : chr [1:6899] "3012-1A41" "3012-1A41" "3012-1A41" "3012-1A41" ...
$ first_name : chr [1:6899] "Leonelle" "Leonelle" "Leonelle" "Leonelle" ...
$ last_name : chr [1:6899] "Simco" "Simco" "Simco" "Simco" ...
$ gender : chr [1:6899] "Female" "Female" "Female" "Female" ...
$ age : num [1:6899] 30 30 30 30 30 30 30 30 30 38 ...
$ business_travel : chr [1:6899] "Some Travel" "Some Travel" "Some Travel" "Some Travel" ...
$ department : chr [1:6899] "Sales" "Sales" "Sales" "Sales" ...
$ distance_from_home_km : num [1:6899] 27 27 27 27 27 27 27 27 27 23 ...
$ state : chr [1:6899] "IL" "IL" "IL" "IL" ...
$ ethnicity : chr [1:6899] "White" "White" "White" "White" ...
$ education : num [1:6899] 5 5 5 5 5 5 5 5 5 4 ...
$ education_field : chr [1:6899] "Marketing" "Marketing" "Marketing" "Marketing" ...
$ job_role : chr [1:6899] "Sales Executive" "Sales Executive" "Sales Executive" "Sales Executive" ...
$ marital_status : chr [1:6899] "Divorced" "Divorced" "Divorced" "Divorced" ...
$ salary : num [1:6899] 102059 102059 102059 102059 102059 ...
$ stock_option_level : num [1:6899] 1 1 1 1 1 1 1 1 1 0 ...
$ over_time : chr [1:6899] "No" "No" "No" "No" ...
$ hire_date : chr [1:6899] "03/01/2012" "03/01/2012" "03/01/2012" "03/01/2012" ...
$ attrition : chr [1:6899] "No" "No" "No" "No" ...
$ years_at_company : num [1:6899] 10 10 10 10 10 10 10 10 10 10 ...
$ years_in_most_recent_role : num [1:6899] 4 4 4 4 4 4 4 4 4 6 ...
$ years_since_last_promotion : num [1:6899] 9 9 9 9 9 9 9 9 9 10 ...
$ years_with_curr_manager : num [1:6899] 7 7 7 7 7 7 7 7 7 0 ...
$ performance_id : chr [1:6899] "PR1295" "PR1908" "PR2617" "PR3436" ...
$ review_date : chr [1:6899] "10/30/2016" "10/30/2017" "10/30/2018" "10/30/2019" ...
$ environment_satisfaction : num [1:6899] 3 4 5 1 3 3 4 4 5 3 ...
$ job_satisfaction : num [1:6899] 3 4 5 3 4 2 5 2 5 3 ...
$ relationship_satisfaction : num [1:6899] 2 5 4 2 2 5 4 4 2 2 ...
$ training_opportunities_within_year: num [1:6899] 3 3 3 3 1 1 1 1 2 2 ...
$ training_opportunities_taken : num [1:6899] 0 1 0 1 0 0 0 0 1 0 ...
$ work_life_balance : num [1:6899] 4 2 4 3 3 3 4 2 5 5 ...
$ self_rating : num [1:6899] 3 3 5 5 4 5 3 5 4 4 ...
$ manager_rating : num [1:6899] 3 2 5 4 3 4 3 4 4 4 ...
- attr(*, "spec")=
.. cols(
.. EmployeeID = col_character(),
.. FirstName = col_character(),
.. LastName = col_character(),
.. Gender = col_character(),
.. Age = col_double(),
.. BusinessTravel = col_character(),
.. Department = col_character(),
.. `DistanceFromHome (KM)` = col_double(),
.. State = col_character(),
.. Ethnicity = col_character(),
.. Education = col_double(),
.. EducationField = col_character(),
.. JobRole = col_character(),
.. MaritalStatus = col_character(),
.. Salary = col_double(),
.. StockOptionLevel = col_double(),
.. OverTime = col_character(),
.. HireDate = col_character(),
.. Attrition = col_character(),
.. YearsAtCompany = col_double(),
.. YearsInMostRecentRole = col_double(),
.. YearsSinceLastPromotion = col_double(),
.. YearsWithCurrManager = col_double()
.. )
- attr(*, "problems")=<externalptr>
colnames(hr_perf_dta) [1] "employee_id" "first_name"
[3] "last_name" "gender"
[5] "age" "business_travel"
[7] "department" "distance_from_home_km"
[9] "state" "ethnicity"
[11] "education" "education_field"
[13] "job_role" "marital_status"
[15] "salary" "stock_option_level"
[17] "over_time" "hire_date"
[19] "attrition" "years_at_company"
[21] "years_in_most_recent_role" "years_since_last_promotion"
[23] "years_with_curr_manager" "performance_id"
[25] "review_date" "environment_satisfaction"
[27] "job_satisfaction" "relationship_satisfaction"
[29] "training_opportunities_within_year" "training_opportunities_taken"
[31] "work_life_balance" "self_rating"
[33] "manager_rating"
library(dplyr)
hr_perf_dta <- hr_perf_dta %>%
mutate(
cat_education = case_when(
education == "No formal education" ~ 1,
education == "High school" ~ 2,
education == "Bachelor" ~ 3,
education == "Masters" ~ 4,
education == "Doctorate" ~ 5,
TRUE ~ NA_real_
)
)
## create cat_envi_sat, cat_job_sat, and cat_relation_sat
hr_perf_dta <- hr_perf_dta %>%
mutate(cat_envi_sat = case_when(
environment_satisfaction == "Very dissatisfied" ~ 1,
environment_satisfaction == "Dissatisfied" ~ 2,
environment_satisfaction == "Neutral" ~ 3,
environment_satisfaction== "Satisfied" ~ 4,
environment_satisfaction == "Very satisfied" ~ 5
),
cat_job_sat = case_when(
job_satisfaction == "Very dissatisfied" ~ 1,
job_satisfaction == "Dissatisfied" ~ 2,
job_satisfaction == "Neutral" ~ 3,
job_satisfaction == "Satisfied" ~ 4,
job_satisfaction == "Very satisfied" ~ 5
),
cat_relation_sat = case_when(
relationship_satisfaction == "Very dissatisfied" ~ 1,
relationship_satisfaction == "Dissatisfied" ~ 2,
relationship_satisfaction == "Neutral" ~ 3,
relationship_satisfaction == "Satisfied" ~ 4,
relationship_satisfaction == "Very satisfied" ~ 5
))
## create cat_work_life_balance, cat_self_rating, and cat_manager_rating
hr_perf_dta <- hr_perf_dta %>%
mutate(cat_work_life_balance = case_when(
work_life_balance == "Unacceptable" ~ 1,
work_life_balance == "Needs improvement" ~ 2,
work_life_balance == "Meets expectation" ~ 3,
work_life_balance == "Exceeds expectation" ~ 4,
work_life_balance == "Above and beyond" ~ 5
),
cat_self_rating = case_when(
self_rating == "Unacceptable" ~ 1,
self_rating == "Needs improvement" ~ 2,
self_rating == "Meets expectation" ~ 3,
self_rating == "Exceeds expectation" ~ 4,
self_rating == "Above and beyond" ~ 5
),
cat_manager_rating = case_when(
manager_rating == "Unacceptable" ~ 1,
manager_rating == "Needs improvement" ~ 2,
manager_rating == "Meets expectation" ~ 3,
manager_rating == "Exceeds expectation" ~ 4,
manager_rating == "Above and beyond" ~ 5
))
## create bi_attrition
hr_perf_dta <- hr_perf_dta %>%
mutate(bi_attrition = case_when(
attrition == "No" ~ 0,
attrition == "Yes" ~ 1
))
## print the updated hr_perf_dta using datatable function
datatable(hr_perf_dta)5 Exploratory data analysis
5.1 Descriptive statistics of employee attrition
Select the variables
attrition,job_role,department,age,salary,job_satisfaction, andwork_life_balance.Save asattrition_key_var_dta.Compute and plot the attrition rate across
job_role,department, andage,salary,job_satisfaction, andwork_life_balance. To compute for the attrition rate, group the dataset by job role. Afterward, you can use thecountfunction to get the frequency of attrition for each job role and then divide it by the total number of observations. Save the computation aspct_attrition. Do not forget to ungroup before storing the output. Store the output asattrition_rate_job_role.Plot for the attrition rate across
job_rolehas been done for you! Study each line of code. You have the freedom to customize your plot accordingly. Show your creativity!
## selecting attrition key variables and save as `attrition_key_var_dta`
attrition_key_var_dta <- hr_perf_dta %>%
select(attrition, job_role, department, age, salary,
job_satisfaction, work_life_balance)
## compute the attrition rate across job_role and save as attrition_rate_job_role
hr_perf_dta <- hr_perf_dta %>%
mutate(bi_attrition = case_when(
attrition == "No" ~ 0,
attrition == "Yes" ~ 1,
TRUE ~ NA_real_
))
attrition_key_var_dta <- hr_perf_dta %>%
select(attrition, job_role, department, age, salary,
job_satisfaction, work_life_balance, bi_attrition)
attrition_rate_job_role <- attrition_key_var_dta %>%
group_by(job_role) %>%
summarise(
total = n(),
attrition_count = sum(bi_attrition, na.rm = TRUE)
) %>%
mutate(pct_attrition = (attrition_count / total) * 100) %>%
ungroup()
## print attrition_rate_job_role
print(attrition_rate_job_role)# A tibble: 13 x 4
job_role total attrition_count pct_attrition
<chr> <int> <dbl> <dbl>
1 Analytics Manager 213 28 13.1
2 Data Scientist 1387 597 43.0
3 Engineering Manager 307 18 5.86
4 HR Business Partner 25 0 0
5 HR Executive 119 29 24.4
6 HR Manager 17 0 0
7 Machine Learning Engineer 582 95 16.3
8 Manager 145 19 13.1
9 Recruiter 152 86 56.6
10 Sales Executive 1567 543 34.7
11 Sales Representative 500 317 63.4
12 Senior Software Engineer 512 84 16.4
13 Software Engineer 1373 445 32.4
## Plot the attrition rate
ggplot(attrition_rate_job_role, aes(x = reorder(job_role, -pct_attrition), y = pct_attrition, fill = job_role)) +
geom_bar(stat = "identity", color = "black", fill = "yellow") +
labs(
title = "Attrition Rate by Job Role",
x = "Job Role",
y = "Attrition Rate (%)"
) +
theme(
panel.background = element_rect(fill = "black"),
plot.background = element_rect(fill = "black"),
panel.grid.major = element_line(color = "gray"),
panel.grid.minor = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, color = "hotpink", size = 10),
axis.text.y = element_text(color = "white", size = 10),
axis.title.x = element_text(color = "white", size = 12, face = "bold"),
axis.title.y = element_text(color = "white", size = 12, face = "bold"),
plot.title = element_text(color = "white", size = 14, face = "bold", hjust = 0.5),
legend.position = "none"
)5.2 Identifying attrition key drivers using correlation analysis
Conduct a correlation analysis of key variables:
bi_attrition,salary,years_at_company,job_satisfaction,manager_rating, andwork_life_balance. Use thecor()function to run the correlation analysis. Remove missing values using thena.omit()before running the correlation analysis. Save the output inhr_corr.Use a correlation matrix or heatmap to visualize the relationship between these variables and attrition. You can use the
GGallypackage and use theggcorrfunction to visualize the correlation heatmap. You may explore this site for more information: ggcorr.Discuss which factors seem most correlated with attrition and what that suggests aobut why employees are leaving.
## conduct correlation of key variables.
key_vars_dta <- hr_perf_dta %>%
select(bi_attrition, salary, years_at_company, job_satisfaction, manager_rating, work_life_balance) %>%
na.omit()
hr_corr <- cor(key_vars_dta)
## print hr_corr
print(hr_corr) bi_attrition salary years_at_company job_satisfaction
bi_attrition 1.000000000 -0.211181478 -0.6896527798 0.0132368129
salary -0.211181478 1.000000000 0.2206442116 0.0053054850
years_at_company -0.689652780 0.220644212 1.0000000000 0.0008700583
job_satisfaction 0.013236813 0.005305485 0.0008700583 1.0000000000
manager_rating -0.007654429 -0.001596736 0.0178656879 -0.0158205481
work_life_balance 0.003428836 -0.001517145 0.0079339508 0.0417242942
manager_rating work_life_balance
bi_attrition -0.007654429 0.003428836
salary -0.001596736 -0.001517145
years_at_company 0.017865688 0.007933951
job_satisfaction -0.015820548 0.041724294
manager_rating 1.000000000 0.007996938
work_life_balance 0.007996938 1.000000000
## install GGally package and use ggcorr function to visualize the correlation
ggcorr(key_vars_dta, label = TRUE, label_size = 3, hjust = 0.75, size = 3, palette = "RdBu", layout.exp = 2) +
labs(title = "Correlation Heatmap of Attrition and Key Variables")::::::::::: callout-note ## Discussion:
Provide your discussion here. :::Factors highly correlated with bi_attrition are likely to suggest drivers for employee attrition.For example, if work_life_balance or job_satisfaction has a strong negative correlation with bi_attrition, it might indicate that poor satisfaction or work-life balance is driving employees to leave.
5.3 Predictive modeling for attrition
Create a logistic regression model to predict employee attrition using the following variables:
salary,years_at_company,job_satisfaction,manager_rating, andwork_life_balance. Save the model ashr_attrition_glm_model. Print the summary of the model using thesummaryfunction.Install the
sjPlotpackage and use thetab_modelfunction to display the summary of the model. You may read the documentation here on how to customize your model summary.Also, use the
plot_modelfunction to visualize the model coefficients. You may read the documentation here on how to customize your model visualization.Discuss the results of the logistic regression model and what they suggest about the factors that contribute to employee attrition.
## run a logistic regression model to predict employee attrition
## save the model as hr_attrition_glm_model
hr_attrition_glm_model <- glm(
bi_attrition ~ salary + years_at_company + job_satisfaction + manager_rating + work_life_balance,
data = hr_perf_dta,
family = binomial
)
## print the summary of the model using the summary function
summary(hr_attrition_glm_model)
Call:
glm(formula = bi_attrition ~ salary + years_at_company + job_satisfaction +
manager_rating + work_life_balance, family = binomial, data = hr_perf_dta)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.571e+00 2.173e-01 11.831 <2e-16 ***
salary -3.633e-06 4.086e-07 -8.893 <2e-16 ***
years_at_company -6.333e-01 1.476e-02 -42.919 <2e-16 ***
job_satisfaction 3.470e-02 3.186e-02 1.089 0.276
manager_rating 5.071e-03 3.810e-02 0.133 0.894
work_life_balance 2.587e-02 3.198e-02 0.809 0.419
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 8574.5 on 6708 degrees of freedom
Residual deviance: 4781.6 on 6703 degrees of freedom
(190 observations deleted due to missingness)
AIC: 4793.6
Number of Fisher Scoring iterations: 5
## install sjPlot package and use tab_model function to display the summary of the model
tab_model(hr_attrition_glm_model, show.ci = TRUE, show.p = TRUE, show.se = TRUE, title = "Logistic Regression Model for Attrition")| bi attrition | ||||
| Predictors | Odds Ratios | std. Error | CI | p |
| (Intercept) | 13.08 | 2.84 | 0.00 – Inf | <0.001 |
| salary | 1.00 | 0.00 | 0.00 – Inf | <0.001 |
| years at company | 0.53 | 0.01 | 0.00 – Inf | <0.001 |
| job satisfaction | 1.04 | 0.03 | 0.00 – Inf | 0.276 |
| manager rating | 1.01 | 0.04 | 0.00 – Inf | 0.894 |
| work life balance | 1.03 | 0.03 | 0.00 – Inf | 0.419 |
| Observations | 6709 | |||
| R2 Tjur | 0.502 | |||
## use plot_model function to visualize the model coefficients
plot_model(hr_attrition_glm_model, show.values = TRUE, title = "Coefficients of Attrition Logistic Regression Model", colors = "navy")::::::::: callout-note ## Discussion:
Provide your discussion here. :::Coefficients: Look at the sign of the coefficients (positive or negative). A positive coefficient suggests that as the predictor increases, the likelihood of attrition also increases. A negative coefficient indicates that as the predictor increases, the likelihood of attrition decreases.P-values: P-values help identify significant predictors. A small p-value (typically <0.05) suggests that the predictor is statistically significant in predicting employee attrition.Odds Ratios (Exp(Coefficient)): If you want to interpret the coefficients as odds ratios, you can exponentiate the coefficients using exp(coef(hr_attrition_glm_model)).
5.4 Analysis of compensation and turnover
Compare the average monthly income of employees who left the company (
bi_attrition = 1) and those who stayed (bi_attrition = 0). Use thet.testfunction to conduct a t-test and determine if there is a significant difference in average monthly income between the two groups. Save the results in a variable calledattrition_ttest_results.Install the
reportpackage and use thereportfunction to generate a report of the t-test results.Install the
ggstatsplotpackage and use theggbetweenstatsfunction to visualize the distribution of monthly income for employees who left and those who stayed. Make sure to map thebi_attritionvariable to thexargument and thesalaryvariable to theyargument.Visualize the
salaryvariable for employees who left and those who stayed usinggeom_histogramwithgeom_freqpoly. Make sure to facet the plot by thebi_attritionvariable and applyalphaon the histogram plot.Provide recommendations on whether revising compensation policies could be an effective retention strategy.
## compare the average monthly income of employees who left and those who stayed
attrition_ttest_results <- t.test(salary ~ bi_attrition, data = hr_perf_dta)
## print the results of the t-test
print(attrition_ttest_results)
Welch Two Sample t-test
data: salary by bi_attrition
t = 18.869, df = 5524.2, p-value < 2.2e-16
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
38577.82 47523.18
sample estimates:
mean in group 0 mean in group 1
125007.26 81956.76
## install the report package and use the report function to generate a report of the t-test results
attrition_ttest_results <- t.test(salary ~ bi_attrition, data = hr_perf_dta)
# Generate a report of the t-test results
report(attrition_ttest_results)Effect sizes were labelled following Cohen's (1988) recommendations.
The Welch Two Sample t-test testing the difference of salary by bi_attrition
(mean in group 0 = 1.25e+05, mean in group 1 = 81956.76) suggests that the
effect is positive, statistically significant, and medium (difference =
43050.50, 95% CI [38577.82, 47523.18], t(5524.24) = 18.87, p < .001; Cohen's d
= 0.51, 95% CI [0.45, 0.56])
# install ggstatsplot package and use ggbetweenstats function to visualize the distribution of monthly income for employees who left and those who stayed
ggbetweenstats(
data = hr_perf_dta,
x = bi_attrition,
y = salary,
title = "Distribution of Monthly Income by Attrition Status",
x.label = "Attrition Status",
y.label = "Monthly Salary",
ggtheme = ggplot2::theme_minimal(),
pairwise.display = "p-adj"
)# create histogram and frequency polygon of salary for employees who left and those who stayed
ggplot(hr_perf_dta, aes(x = salary, fill = as.factor(bi_attrition))) +
geom_histogram(alpha = 0.7, position = "identity", bins = 30, color = "white") +
geom_freqpoly(aes(color = as.factor(bi_attrition)), size = 1.5, bins = 30) +
facet_wrap(~ bi_attrition, labeller = as_labeller(c("0" = "Stayed", "1" = "Left"))) +
labs(
title = "Salary Distribution by Attrition Status",
subtitle = "Comparing Employees Who Stayed vs. Left",
x = "Monthly Salary",
y = "Count",
fill = "Attrition Status",
color = "Attrition Status"
) +
scale_fill_manual(values = c("#4DAF4A", "#E41A1C")) + # Custom fill colors
scale_color_manual(values = c("#377EB8", "#FF7F00")) + # Custom line colors
theme_minimal(base_size = 15) + # Increase base font size
theme(
plot.title = element_text(size = 20, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 16, hjust = 0.5),
strip.text = element_text(size = 14, face = "bold"),
legend.position = "top",
panel.grid.major = element_line(color = "gray90", size = 0.5),
panel.grid.minor = element_blank()
)::: callout-note ## Discussion:
Provide your discussion here. :::If the t-test indicates a significant difference in salary between those who left and those who stayed, this may suggest that compensation is a significant factor in employee retention.If employees who left had significantly lower salaries, it could be beneficial to review and possibly revise compensation policies to ensure they are competitive and equitable.Implementing regular salary reviews and ensuring alignment with industry standards can help improve employee satisfaction and reduce turnover.
5.5 Employee satisfaction and performance analysis
Analyze the average performance ratings (both
ManagerRatingandSelfRating) of employees who left vs. those who stayed. Use thegroup_byandcountfunctions to calculate the average performance ratings for each group.Visualize the distribution of
SelfRatingfor employees who left and those who stayed using a bar plot. Use theggplotfunction to create the plot and map theSelfRatingvariable to thexargument and thebi_attritionvariable to thefillargument.Similarly, visualize the distribution of
ManagerRatingfor employees who left and those who stayed using a bar plot. Make sure to map theManagerRatingvariable to thexargument and thebi_attritionvariable to thefillargument.Create a boxplot of
salarybyjob_satisfactionandbi_attritionto analyze the relationship between salary, job satisfaction, and attrition. Use thegeom_boxplotfunction to create the plot and map thesalaryvariable to thexargument, thejob_satisfactionvariable to theyargument, and thebi_attritionvariable to thefillargument. You need to transform thejob_satisfactionandbi_attritionvariables into factors before creating the plot or within theggplotfunction.Discuss the results of the analysis and provide recommendations for HR interventions based on the findings.
# Analyze the average performance ratings (both ManagerRating and SelfRating) of employees who left vs. those who stayed.
avg_self_rating <- hr_perf_dta %>%
group_by(bi_attrition) %>%
summarise(avg_self_rating = mean(self_rating, na.rm = TRUE))
# Print the result
avg_self_rating# A tibble: 2 x 2
bi_attrition avg_self_rating
<dbl> <dbl>
1 0 3.98
2 1 3.99
avg_manager_rating <- hr_perf_dta %>%
group_by(bi_attrition) %>%
summarise(avg_manager_rating = mean(manager_rating, na.rm = TRUE))
# Print the result
avg_manager_rating# A tibble: 2 x 2
bi_attrition avg_manager_rating
<dbl> <dbl>
1 0 3.48
2 1 3.46
hr_perf_sample <- hr_perf_dta %>% sample_n(1000)
# Group by bi_attrition and calculate average performance ratings
average_ratings <- hr_perf_dta %>%
group_by(bi_attrition) %>%
summarise(
avg_self_rating = mean(self_rating, na.rm = TRUE),
avg_manager_rating = mean(manager_rating, na.rm = TRUE)
)
# Print the result
average_ratings# A tibble: 2 x 3
bi_attrition avg_self_rating avg_manager_rating
<dbl> <dbl> <dbl>
1 0 3.98 3.48
2 1 3.99 3.46
# Visualize the distribution of SelfRating for employees who left and those who stayed using a bar plot.
ggplot(hr_perf_dta, aes(x = as.factor(self_rating), fill = as.factor(bi_attrition))) +
geom_bar(position = "dodge", alpha = 0.7) +
labs(
title = "Distribution of Self-Rating by Attrition Status",
x = "Self Rating",
y = "Count",
fill = "Attrition Status"
) +
scale_fill_manual(values = c("black", "navyblue")) +
theme_minimal(base_size = 15) +
theme(
plot.title = element_text(size = 20, face = "bold", hjust = 0.5),
legend.position = "top"
)# Visualize the distribution of ManagerRating for employees who left and those who stayed using a bar plot.
ggplot(hr_perf_dta, aes(x = as.factor(manager_rating), fill = as.factor(bi_attrition))) +
geom_bar(position = "dodge", alpha = 0.7) +
labs(
title = "Distribution of Manager-Rating by Attrition Status",
x = "Manager Rating",
y = "Count",
fill = "Attrition Status"
) +
scale_fill_manual(values = c("purple", "hotpink")) +
theme_minimal(base_size = 15) +
theme(
plot.title = element_text(size = 20, face = "bold", hjust = 0.5),
legend.position = "below"
)# create a boxplot of salary by job_satisfaction and bi_attrition to analyze the relationship between salary, job satisfaction, and attrition.
hr_perf_dta$job_satisfaction <- as.factor(hr_perf_dta$job_satisfaction)
hr_perf_dta$bi_attrition <- as.factor(hr_perf_dta$bi_attrition)
ggplot(hr_perf_dta, aes(x = job_satisfaction, y = salary, fill = bi_attrition)) +
geom_boxplot(alpha = 0.7) +
labs(
title = "Salary Distribution by Job Satisfaction and Attrition Status",
x = "Job Satisfaction",
y = "Salary",
fill = "Attrition Status"
) +
scale_fill_manual(values = c("yellow", "lightblue")) +
theme_minimal(base_size = 15) +
theme(
plot.title = element_text(size = 20, face = "bold", hjust = 0.5),
legend.position = "top"
)::: callout-note ## Discussion:
Provide your discussion here. :::For the performance ratings the analysis shows how employees who left have different average self and manager ratings compared to those who stayed. If there is a significant difference in these ratings, HR may consider coaching or management interventions to improve satisfaction. For the salary and job satisfaction the boxplot shows that employees with lower job satisfaction or those with lower salary tend to leave, HR could focus on salary adjustments or satisfaction-based programs to retain talent.
5.6 Work-life balance and retention strategies
::: callout-tip ## Task 5.6. Analyzing work-life balance and retention strategies
At this point, you are already well aware of the dataset and the possible factors that contribute to employee attrition. Using your R skills, accomplish the following tasks:
- Analyze the distribution of WorkLifeBalance ratings for employees who left versus those who stayed.
work_life_balance_distribution <- hr_perf_dta %>%
group_by(bi_attrition) %>%
summarise(
count = n(),
avg_work_life_balance = mean(work_life_balance, na.rm = TRUE),
sd_work_life_balance = sd(work_life_balance, na.rm = TRUE)
)
# Print the distribution results
work_life_balance_distribution# A tibble: 2 x 4
bi_attrition count avg_work_life_balance sd_work_life_balance
<fct> <int> <dbl> <dbl>
1 0 4638 3.41 1.15
2 1 2261 3.42 1.14
- Use visualizations to show the differences.
ggplot(hr_perf_dta, aes(x = factor(work_life_balance), fill = as.factor(bi_attrition))) +
geom_bar(position = "dodge") +
labs(
title = "Distribution of Work-Life Balance Ratings by Attrition Status",
x = "Work-Life Balance Rating",
y = "Count",
fill = "Attrition Status"
) +
scale_fill_manual(values = c("limegreen", "black")) +
theme_minimal(base_size = 15) +
theme(
plot.title = element_text(size = 20, face = "bold"),
legend.position = "top"
)- Assess whether employees with poor work-life balance are more likely to leave.
work_life_balance_ttest <- t.test(work_life_balance ~ bi_attrition, data = hr_perf_dta)
# Print t-test results
work_life_balance_ttest
Welch Two Sample t-test
data: work_life_balance by bi_attrition
t = -0.28121, df = 4562.3, p-value = 0.7786
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
-0.06614473 0.04954960
sample estimates:
mean in group 0 mean in group 1
3.411871 3.420168
You have the freedom how you will accomplish this task. Be creative and provide insights that will help HR develop effective retention strategies. :::Identify Critical Ratings- If employees who left reported significantly lower work-life balance ratings, this suggests that work-life balance is a critical factor in attrition.Flexible Work Arrangements- Implement flexible working hours or remote work options to help employees manage their work-life balance better.Work-Life Balance Programs- Consider introducing wellness programs that encourage healthy work-life balance practices, such as mindfulness training, fitness classes, and time-off policies.Regular Feedback- Conduct regular employee surveys to assess work-life balance perceptions and address concerns promptly.Management Training: Train managers to recognize signs of employee burnout and promote work-life balance within their teams.
5.7 Recommendations for HR interventions
::: callout-tip ## Task 5.7. Recommendations for HR interventions
Based on the analysis conducted, provide recommendations for HR interventions that could help reduce employee attrition and improve overall employee satisfaction and performance. You may use the following question as guide for your recommendations and discussions.
What are the key factors contributing to employee attrition in the company? ::: The analysis revealed that compensation, job satisfaction, and work-life balance are critical factors influencing employee attrition. Employees with lower salaries and poor job satisfaction ratings were significantly more likely to leave the company.
Which factors are most strongly correlated with attrition? :::Salary showed a strong negative correlation with attrition, indicating that competitive compensation is essential for retention. Also job satisfaction and work-life balance were positively correlated with retention, highlighting their importance in employee satisfaction.
What strategies could be implemented to improve employee retention and satisfaction? :::HR should implement competitive compensation packages and enhance job satisfaction through feedback mechanisms and career development opportunities and promoting work-life balance through flexible work options and wellness programs can significantly improve employee retention.
How can HR leverage the insights from the analysis to develop effective retention strategies? :::HR can use data-driven insights to create targeted interventions aimed at reducing attrition in critical areas identified in the analysis. Tailoring programs to address specific employee segments will ensure that interventions are relevant and effective.
What are the potential benefits of implementing these strategies for the company? :::Reduced Turnover Costs: By decreasing attrition rates, the company can save on recruitment, onboarding, and training expenses associated with new hires. :::Improved Employee Morale: Satisfied employees are likely to be more engaged and motivated, leading to enhanced productivity and performance. :::Stronger Employer Brand: Positive workplace culture and low turnover rates can help attract top talent, enhancing the company’s reputation in the job market. :::Long-term Growth: Implementing effective retention strategies fosters loyalty among employees, which can contribute to the company’s long-term success and stability.